A knowledge-based scheduling system for Emergency Departments

A knowledge-based reactive scheduling system is proposed to answer the requirements of Emergency Departments (EDs). The algorithm includes detailed patient priority, arrival time, flow time and doctor load. The main aim is to determine the patients who have higher priorities initially, and then minimize their waiting times. To achieve this aim, physicians and the other related workers can use an interactive system. In this study, we evaluated the existing system by comparing the proposed system. Also, reactive scheduling cases were evaluated for some items such as decreasing the number of doctors, changing durations and entering of an urgent patient to the system. All experiments were performed with proposed algorithm and right shift rescheduling approach.     

Libra: a high fidelity knowledge-based system for recommending scheduling programs

Contemporary knowledge systems are usually neither easy to use or understand. We propose a new class of knowledge systems, which we call high fidelity systems, that use representations and processes that closely resemble the objects and tasks with which people are already familiar. To illustrate the concept of high fidelity, we describe Libra, one such system for recommending scheduling programs. Our initial experience with Libra and its usage suggests that high fidelity not only enhances the user acceptability of knowledge-based systems, but it also facilities high speed design and implementation.We chose to replicate the knowledge in Libra because it is already published, and because we wanted to show that a Libra user can have an interface similar to the diagrams drawn in the journal paper. The latter was used to describe the knowledge-based framework for selecting scheduling systems. If we can replicate them in the user interface, we would have successfully created a high fidelity system — that is, the Libra user interface would be at least as able to communicate the framework to the user as the journal paper.     

A knowledge-based system for reactive scheduling decision-making in FMS

This paper describes research into the development of an intelligent simulation environment. The environment was used to analyze reactive scheduling scenarios in a specific flexible manufacturing systems (FMS) configuration. Using data from a real FMS, simulation models were created to study the reactive scheduling problem and this work led to the concept of capturing instantaneous FMS status data as snapshot data for analysis. Various intelligent systems were developed and tested to asses their decision-making capabilities. The concepts of History Logging and expert system learning is proposed and these ideas are implemented into the environment to provide decision-making and control across a FMS schedule lifetime. This research proposes an approach for the analysis of reactive scheduling in an FMS. The approach and system that was subsequently developed was based on the principle of automated intelligent decision-making via knowledge elicitation from FMS status data, together with knowledge base augmentation to facilitate a learning ability based on past experiences.     

A self-adaptive agent-based fuzzy-neural scheduling system for a wafer fabrication factory

A self-adaptive agent-based fuzzy-neural system is constructed in this study to enhance the performance of scheduling jobs in a wafer fabrication factory. The system integrates dispatching, performance evaluation and reporting, and scheduling policy optimization. Unlike in the past studies a single pre-determined scheduling algorithm is used for all agents, in this study every agent develops and modifies its own scheduling algorithm to adapt it to the local conditions. To stabilize the performance of the self-adaptive agent-based fuzzy-neural scheduling system, some treatments have also been taken. To evaluate the effectiveness of the proposed methodology and to make comparison with some existing approaches, production simulation is also applied in this study to generate some test data. According to experimental results, the self-adaptive agent-based fuzzy-neural system did improve the performance of scheduling jobs in the simulated wafer fabrication factory, especially with respect to the average cycle time and cycle time standard deviation.     

Intelligent scheduling approaches for a wafer fabrication factory

The production system of a wafer fabrication factory is a very complicated process. Job scheduling in a wafer fabrication factory is a very difficult task. To solve this problem, two intelligent scheduling rules are proposed in this study. The intelligent scheduling rules are modified from the well-known fluctuation smoothing rules with some innovative treatments. To evaluate the effectiveness of the proposed methodology, production simulation was also applied in this study. According to experimental results, the proposed methodology outperformed some existing approaches by reducing the average cycle time and cycle time standard deviation, the most important objectives of job scheduling in a wafer fabrication factory.     

基于UML的适应调度知识系统开发

对复杂制造系统进行有效的适应调度，构建性能优良的知识系统是基础和核心。采用统一建模语言对适应调度知识系统的分析、设计和实现进行描述，建立了反映知识系统静态结构和动态行为的各种视图，使用对象约束语言对在系统设计中出现的类对象进行精确语义约束。使用这种建模技术减小了对领域知识专家的严重依赖，提高了知识系统开发的质量和效率。最后给出了应用实例验证该方法的可行性。     

An object-oriented architecture for knowledge-based production scheduling systems

This paper describes an object-oriented architecture to support decision making in production scheduling environments. An object-oriented world view is used to integrate concepts from discrete event simulation, conventional scheduling logic and artificial intelligence to produce capacity-feasible schedules. The architecture was implemented as a collection of loosely coupled reusable software objects by extending the functionality of software objects from BLOCS/M (Berkeley Library of Objects for Control and Simulation of Manufacturing). Our experience with an industrial prototype is presented.     

.基于知识系统的不确定性模型

研究基于知识系统（KBS）中知识的不确定性是开发KBS的重要问题,但现有模型化KBS几乎都是基于确定性知识的。以经典的CommonKADS模型为背景,采用模型化工程中的不确定性技术,研究KBS中不确定性知识的表示方法。首先在基于值系统的值集概念上引入假设函数集合的评估函数,定义静态不确定性领域知识;而后采用因果模型描述动态的不确定性推理知识和任务知识;最后将三类不确定性知识映射至CommonKADS模型。由此给出了描述不确定KBS的通用模型,扩展了KBS的可用性。     

An investigation into knowledge-based yard crane scheduling for container terminals

To resolve the problems of operational efficiency, energy consumption and operational cost of an entire container terminal, the yard crane scheduling secures a crucial position during terminal operational process. Accordingly, it is imperative to develop an efficient yard crane scheduling strategy. In this study, the knowledge acquisition was initially conducted. Subsequently, a knowledge sorting process, including the taxonomic tree generation and organization of acquired knowledge, was completed. Afterwards, the rules were extracted for the purpose of yard crane scheduling. Furthermore, a mechanism was deployed for knowledge reasoning. Consequently, a knowledge-based system was established with regard to yard crane scheduling. To this end, a case study was used to illustrate the proposed knowledge-based system.     

Development of a cloud-based factory simulation system for enabling ubiquitous factory simulation

This study investigated several problems related to the implementation of cloud-based factory simulation. First, the differences between cloud-based factory simulation and parallel and distributed factory simulation were discussed. Individually managed, resource heterogeneity, uneven load partitioning, and potential business opportunities were found to be the novel characteristics that discriminate cloud-based factory simulation from parallel and distributed factory simulation. The problems in existing cloud-based factory simulation systems are discussed. An experimental cloud-based factory simulation system was developed and used for simulating a mobile lift table factory.     

A unified view on planning,scheduling and dispatching for a factory

In a smart factory, the decisions of planning, scheduling and dispatching are made based on the real time information through Internet-of-Things. To ensure the organization objective can be carried out through different levels faithfully, planning, scheduling and dispatching should be considered through a cyber physical production system in an integrated manner. To support the implementation of a smart factory, their definitions have to be given through a unified view. The distinction between planning and scheduling is given based on microeconomics and queueing theory. The distinction between scheduling and dispatching is analyzed from the viewpoint of computational complexity and hierarchical decomposition. Based on the elasticity of price and capacity, planning can be separated into demand planning or capacity planning. Scheduling period is the time horizon where price and average production costs are insensitive to the production rate. The critical roles of the master production schedule and throughput targets in job scheduling have been explained through the concept of hierarchical decomposition. Dispatching is the last layer of scheduling in the hierarchical decomposition.     

Student modelling by a knowledge-based system

ELECTRE is a project to build an intelligent tutoring system for learning basic electricity. This paper describes a student model based on the student's cognitive processes. This model includes, for each student, his/her domain knowledge and the specific heuristics. Moreover, it uses meta-knowledge of problem solving. This model is simulated by a knowledge-based system that controls the solving processes by meta-rules. A case study is presented.     

KBMS: a knowledge-based system for modeling software systemspecifications

Recent research results in software engineering have shown that the process of formalizing and automating software specification activity is moving towards knowledge-based specification systems. A modeling approach and a knowledge-based system that assist in the requirement acquisition, development, and maintenance of such software system specifications are presented     

Heuristic factory planning algorithm for advanced planning and scheduling

This study focuses on solving the factory planning (FP) problem for product structures with multiple final products. In situations in which the capacity of the work center is limited and multiple job stages are sequentially dependent, the algorithm proposed in this study is able to plan all the jobs, while minimizing delay time, cycle time, and advance time. Though mixed integer programming (MIP) is a popular way to solve supply chain factory planning problems, the MIP model becomes insolvable for complex FP problems, due to the time and computer resources required. For this reason, this study proposes a heuristic algorithm, called the heuristic factory planning algorithm (HFPA), to solve the supply chain factory planning problem efficiently and effectively. HFPA first identifies the bottleneck work center and sorts the work centers according to workload, placing the work center with the heaviest workload ahead of the others. HFPA then groups and sorts jobs according to various criteria, for example, dependency on the bottleneck work center, the workload at the bottleneck work center, and the due date. HFPA plans jobs individually in three iterations. First, it plans jobs without preempting, advancing, and/or delaying. Jobs that cannot be scheduled under these conditions are scheduled in the second iteration, which allows preemption. In the final iteration, which allows jobs to be preempted, advanced, and delayed, all the remaining jobs are scheduled. A prototype was constructed and tested to show HFPA's effectiveness and efficiency. This algorithm's power was demonstrated using computational and complexity analysis.     

A knowledge-based system for disk-drive diagnostics

A frame-based, backward-chaining knowledge-based system was developed to help the diagnostic process in the manufacture of computer high-end disk drives. The nature of the diagnostic and testing process is presented and the methodology used to develop the knowledge-based system is explained. The development and consultation environments are introduced and the final system is described. The improvements and results obtained with the utilization of this knowledge-based system in a factory environment are also included.     

A system for knowledge-based process planning

We describe the application of problem solving, knowledge based methods in creating process plans in manufacturing. The planner presented - called TOLTEC - is designed for experiential domains and bases its operation on the use of cases in a dynamic memory environment. We will describe the way TOLTEC creates process plans by utilizing previous experiences, dynamic clustering of its memories and dynamic constraint generation and by shifting its focus of attention to different features of the workpiece by using importance values. Also, we will present how TOLTEC learns by modifying its memories according to new experiences and how it helps bridge some of the gap between design and manufacturing by detecting design errors.The emphasis in this paper is more on the application aspects of our system and the examples presented will demonstrate the abilities of TOLTEC to design process plans, detect design errors, predict manufacturing errors, recover from planning errors, handle multiple branching solutions and improve its performance by utilizing learning techniques.     

A knowledge-based system for visualization design

Vista is a knowledge-based system that helps scientists design visualization techniques. It generates a technique for a given data set and lets users modify the design interactively using a compositional design methodology. To ensure the effectiveness of its designs, Vista uses many rules, mostly heuristic in nature, that were acquired through literature surveys and discussions with visualization experts. In general, Vista's design was based on research in graphical perception. It extends the design methodology of Automatic Presentation Tool (APT) (J.D. Mckinlay, 1986), a presentation tool for 2D graphics, to three dimensions. While Vista's primary function is to automatically generate an effective visualization technique design for a given data set, it also allows users to interactively modify this design and renders the resulting image using a variety of rendering algorithms